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04087nam a22004935i 4500 |
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978-3-642-17875-7 |
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DE-He213 |
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20151125201304.0 |
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110131s2011 gw | s |||| 0|eng d |
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|a 9783642178757
|9 978-3-642-17875-7
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|a 10.1007/978-3-642-17875-7
|2 doi
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|a Rigatos, Gerasimos G.
|e author.
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|a Modelling and Control for Intelligent Industrial Systems
|h [electronic resource] :
|b Adaptive Algorithms in Robotics and Industrial Engineering /
|c by Gerasimos G. Rigatos.
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| 264 |
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2011.
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| 300 |
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|a XXX, 380 p. 220 illus., 134 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a text file
|b PDF
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|a Intelligent Systems Reference Library,
|x 1868-4394 ;
|v 7
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|a Industrial robots in contact-free operation -- Industrial robots in compliance tasks -- Mobile robots and autonomous vehicles -- Adaptive control methods for industrial systems .-Robust control methods for industrial systems -- Filtering and estimation methods for industrial systems -- Sensor fusion-based control for industrial systems -- Fault detection and isolation for industrial systems -- Application of fault diagnosis to industrial systems -- Optimization methods for motion planning of multi-robot systems -- Optimization methods for target tracking by multi-robot systems -- Optimization methods for industrial automation -- Machine learning methods for industrial systems control -- Machine learning methods for industrial systems fault diagnosis -- Applications of machine vision to industrial systems.
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|a Incorporating intelligence in industrial systems can help to increase productivity, cut-off production costs, and to improve working conditions and safety in industrial environments. This need has resulted in the rapid development of modeling and control methods for industrial systems and robots, of fault detection and isolation methods for the prevention of critical situations in industrial work-cells and production plants, of optimization methods aiming at a more profitable functioning of industrial installations and robotic devices and of machine intelligence methods aiming at reducing human intervention in industrial systems operation. To this end, the book analyzes and extends some main directions of research in modeling and control for industrial systems. These are: (i) industrial robots, (ii) mobile robots and autonomous vehicles, (iii) adaptive and robust control of electromechanical systems, (iv) filtering and stochastic estimation for multisensor fusion and sensorless control of industrial systems (iv) fault detection and isolation in robotic and industrial systems, (v) optimization in industrial automation and robotic systems design, and (vi) machine intelligence for robots autonomy. The book will be a useful companion to engineers and researchers since it covers a wide spectrum of problems in the area of industrial systems. Moreover, the book is addressed to undergraduate and post-graduate students, as an upper-level course supplement of automatic control and robotics courses.
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| 650 |
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|a Engineering.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Computational intelligence.
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|a Robotics.
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|a Automation.
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|a Engineering.
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| 650 |
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|a Computational Intelligence.
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| 650 |
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|a Robotics and Automation.
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| 650 |
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|a Artificial Intelligence (incl. Robotics).
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| 710 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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| 776 |
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|i Printed edition:
|z 9783642178740
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| 830 |
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|a Intelligent Systems Reference Library,
|x 1868-4394 ;
|v 7
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/978-3-642-17875-7
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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